Research Article

Accurate Conditional Variance Models for Predicting Asymmetric Volatility in Cryptocurrency Markets

Volume: 39 Number: 4 December 12, 2024
EN TR

Accurate Conditional Variance Models for Predicting Asymmetric Volatility in Cryptocurrency Markets

Abstract

This study includes tests on the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model and its derivatives to conduct complex and detailed volatility analysis for the 5 highest-volume cryptocurrencies traded in September 2023. The tests have been conducted with Python, R, and Eviews software and analyses have been compared in terms of consistency and accuracy of the results across multiple software and programming languagse. In the testing process, observation of the volatility has been assessed by some variables such as skewness, kurtosis, and log-likelihood values, and these variables have been taken into consideration for testing. Tests such as Jarque-Bera and Augmented Dickey-Fuller (ADF) have been applied during the process to verify model correctness. The EGARCH, GJR-GARCH, and TGARCH models have been more effective in detecting volatility and market shocks in the relevant cryptocurrencies as a result of the tests conducted in the volatility analysis.

Keywords

References

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Details

Primary Language

English

Subjects

Economic Models and Forecasting, Time-Series Analysis

Journal Section

Research Article

Early Pub Date

November 11, 2024

Publication Date

December 12, 2024

Submission Date

February 8, 2024

Acceptance Date

April 30, 2024

Published in Issue

Year 2024 Volume: 39 Number: 4

APA
Çelebi, O., & Demireli, E. (2024). Accurate Conditional Variance Models for Predicting Asymmetric Volatility in Cryptocurrency Markets. İzmir İktisat Dergisi, 39(4), 909-930. https://doi.org/10.24988/ije.1434189
AMA
1.Çelebi O, Demireli E. Accurate Conditional Variance Models for Predicting Asymmetric Volatility in Cryptocurrency Markets. İzmir İktisat Dergisi. 2024;39(4):909-930. doi:10.24988/ije.1434189
Chicago
Çelebi, Onur, and Erhan Demireli. 2024. “Accurate Conditional Variance Models for Predicting Asymmetric Volatility in Cryptocurrency Markets”. İzmir İktisat Dergisi 39 (4): 909-30. https://doi.org/10.24988/ije.1434189.
EndNote
Çelebi O, Demireli E (December 1, 2024) Accurate Conditional Variance Models for Predicting Asymmetric Volatility in Cryptocurrency Markets. İzmir İktisat Dergisi 39 4 909–930.
IEEE
[1]O. Çelebi and E. Demireli, “Accurate Conditional Variance Models for Predicting Asymmetric Volatility in Cryptocurrency Markets”, İzmir İktisat Dergisi, vol. 39, no. 4, pp. 909–930, Dec. 2024, doi: 10.24988/ije.1434189.
ISNAD
Çelebi, Onur - Demireli, Erhan. “Accurate Conditional Variance Models for Predicting Asymmetric Volatility in Cryptocurrency Markets”. İzmir İktisat Dergisi 39/4 (December 1, 2024): 909-930. https://doi.org/10.24988/ije.1434189.
JAMA
1.Çelebi O, Demireli E. Accurate Conditional Variance Models for Predicting Asymmetric Volatility in Cryptocurrency Markets. İzmir İktisat Dergisi. 2024;39:909–930.
MLA
Çelebi, Onur, and Erhan Demireli. “Accurate Conditional Variance Models for Predicting Asymmetric Volatility in Cryptocurrency Markets”. İzmir İktisat Dergisi, vol. 39, no. 4, Dec. 2024, pp. 909-30, doi:10.24988/ije.1434189.
Vancouver
1.Onur Çelebi, Erhan Demireli. Accurate Conditional Variance Models for Predicting Asymmetric Volatility in Cryptocurrency Markets. İzmir İktisat Dergisi. 2024 Dec. 1;39(4):909-30. doi:10.24988/ije.1434189
İzmir Journal of Economics
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